A Gaussian process (GP) is a flexible probabilistic model that captures the covariances between the samples and is commonly used in regression as well as classification tasks [1]}, [2]}. GPs have become popular machine learning methods especially in modelling time-series data [2]}, [4]}. Gaussian process latent variable model (GPLVM) is a GP variant that is specifically designed for embedding high-dimensional data points into a low-dimensional space [5]}. Covariate GPLVM can utilise auxiliary covariates in adjusting the embedding [6]}.
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